Wireless service providers are experiencing an exponential growth in wireless data usage. This has left wireless service providers with the sizable task of coping with unprecedented levels of infrastructure strain while simultaneously facing an ever-increasing gap between data usage and generated revenue. This ongoing congestion concern has been exacerbated by the growing use of devices (such as smartphones, data heavy applications, and USB dongles), which ultimately impact customer quality of service. As a result, service providers are now turning to new and innovative solutions for maintaining client service quality while minimizing incurred costs.
One such wireless service provider solution involves bypassing congested and expensive cellular networks by connecting to and streaming data over the growing number of non-cellular access points, such as WiMAX and WLAN. This process of transferring cellular network connections and data flows to other networks to reduce congestion is known as data offload. Ultimately, this requires a connection manager on a device, governed by offloading policy, to facilitate the transfer of data traffic and network connections between cellular and non-cellular networks.
Many data offload and policy enforcement processes rely heavily on knowing the device's location. It is one of the most common measures for determining whether or not a particular policy should be enforced. However, existing methods for determining device location have some inherent issues. GPS radios used in devices drain batteries quickly and GPS locations are often inaccurate, especially in dense urban areas. Finding device location based on a triangulation process with nearby cellular towers or Wi-Fi access point signals are likewise often inaccurate.
Inaccuracies in device location are problematic for data offloading solutions that attempt to make very precise decisions about when and how wireless devices should transition over to Wi-Fi networks. Hence, this embodiment proposes a novel method of servicing this need without needing to geographically locate the wireless device. Instead, the embodiment describes data offloading processes that leverages a notion of location relative to the wireless environment.
In general, the term connection management is used to denote the decision process undertaken within each wireless device to determine which of the available physical wireless networks or network access points it will connect to for its network services at any given point in time, as well as the subsequent connection enforcement processes. Currently, connection management is performed either:                direct user control.        connection decisions enacted by a user installed connection management application acting under the direction of user instantiated connection policies.        in conjunction with a network-level offload controller that downloads (or pushes) connection policies to the wireless device's installed connection management application. Offload controllers are usually situated within a service provider's core network and govern the timing, content, and delivery of wireless service provider policies. For example to mitigate network overload events or manage other network quality of service issues, i.e., as when 3G/4G carriers offload subscribers to available IEEE 802.11x access points to manage network loads within the 3G/4G network.        
A policy is a set of rules or a course/principle of action to guide or govern decisions, functionality, or operation of a device.
Systems designed to manage the network connections of wireless devices operate based on connection policies. These outline the actions to be taken when certain conditions are met. Many of these systems make use of a policy type that sets a prioritized list of networks for connections. This lets service providers ensure that their access points are always preferred by their clients. It also allows them to set preferred roaming partners. For wireless devices users, it provides a mechanism for prioritizing the use of known home or work access points.
As an example, a network prioritization policy may list the following:                1. User's self defined access points.        2. Home service provider's owned access points.        3. 1st preferred roaming partner        4. 2nd preferred roaming partner        
Offloading policy must be appropriately enforced by connection managers to ensure that data offload does not interrupt the device's user experience, and that their expected quality of service is provided on the new access network. State-of-the-art in offloading presupposes relatively static policies that are structured to manage issues within the context of a specific communications network, where the access points to which wireless devices connect to comprise one aspect of this network.
The current state-of-the-art does not comprise the development of advanced offloading profiles based on:                a composite of information collected from a multiplicity of networks and network access points.        a composite of information available from 3rd party sources (i.e., non-network and non-wireless device based information sourced).        a composite of information available from a multiplicity of wireless devices, whether or not they are receiving wireless network services.        the use of these information sources to assess, guide, or otherwise inform the creation, modification, or restructuring of offload decision polices.        
To date, offload controllers have been limited in scope and have not fully leveraged the improvements to policy enforcement processes that are achievable through the dynamic analysis of operations data from all related devices and systems. The enhancement of policy enforcement through data aggregation and analysis is a core area of focus of the embodiment. Specifically, the area of interconnected wireless networks, has been under-explored due to the technical complexity and required advanced understanding of how inter-device and server/cloud collaboration can be leveraged to achieve measurable offload controller improvements through an analytic feedback loop. This feedback loop could be used, for example, to aggregate offload controller data that may then be processed and returned to the offload controller in the form of profiles, which are useful in subsequent policy enforcement decisions. It is clear that offload controllers have not begun to realize their full potential.
Most prior art depicts offload controllers having client-side implementations with no server collaboration, inter-device data exchange, or interaction with a policy-managing interface, such as a policy-managing server. Some offload controllers use advanced processes and methods to determine how and when policies are best enforced, but these policies do not change dynamically with network, device, or user conditions.
For endpoint wireless devices making a connection to a network, the notion of monitoring, collecting and reporting connection data and/or attributes for use in analyzing user behavior and device connectivity efficiencies may be considered. However, the data collected is not used to modify, inform, or generate offload controller policy.
Prior art/publications generally view profiles as being ostensibly static client-based descriptions set by users, service providers or OEMs. Some prior art discusses dynamic profile generation; however these publications have failed to explore how dynamic profiles may be generated in such a way that they may add value to the algorithmic processes being performed by offload controllers and policy managers. Instead they simply describe collecting data. There is no discussion of then distributing the resulting analytics to provide system improvements to the data collection source entities. ANDSF, HotSpot 2.0, GAS, ANQP, IEEE 802.11u, IEEE 802.11v, pre-loaded Connection Managers and systems without direct connection manager to policy server communication all support policies for setting a prioritized list of networks or access points to connect to. However to date prioritization policies are almost exclusively being used to select a prioritized list of access networks. Most commonly, connection managers are also only provided with one or two prioritization levels. One is reserved for the wireless device users owned or trusted access points. The other is for the set of access points owned by the wireless device's wireless service provider. If neither of those options is available, the connection manager must rely on direct user input to select appropriate network connections. Despite this, standards like ANDSF and IEEE 802.11v currently support over 250 unique priority levels of access networks or access points that can be enforced through connection policy.
In systems where the policy server communicates directly with access points instead of connection managers, network prioritization policies are used to restrict access to certain access points in order to force a device onto other access points within the same network. This is largely done for load balancing purposes within a single network. Network prioritization policies in this area are not being used to improve data offloading processes, where data offload generally refers to having wireless devices switch between GSM and small cell networks to alleviate congestion events.
Service providers currently rely on network analytics for everything from engineering operations planning to designing targeted marketing campaigns. It is important to note that current network analytics mainly rely on network-side measures for analysis; they do not capture information from the wireless device itself, and hence they produce network-side measures. These network-side measures are not always reflective of what is actually being experienced by the device. For this reason, performance readings measured directly on the device are more reflective of what the user is actually experiencing than readings taken anywhere else. Many issues give rise to the differences between network-side and device-side readings, one of which is wireless signal interference. It is also important to note that current network analytics are not designed specifically for the needs of offloading solutions or the users of offloading solutions.
Existing offload solution control interfaces for service providers, or dashboards, are quite basic. They allow the service provider to select the policies they would like to push to devices when certain location and network congestion conditions are met. They do not possess any network assessment, policy/offloading solution assessment, or informed policy design functions. Hence, these Dashboard interfaces are not overly functional or advanced.
Cellular wireless communication networks include, but are not limited to, such protocols as: Code Division Multiple Access (CDMA) cellular radiotelephone communication systems, Global System for Mobile Communications (GSM) cellular radiotelephone systems, North American Digital Cellular (NADC) cellular radiotelephone systems, Time Division Multiple Access (TDMA) systems, Extended-TDMA (E-TDMA) cellular radiotelephone systems, third generation (3G) systems like Wide-band CDMA (WCDMA), and CDMA-2000.
In addition, wireless communication devices may also include multiple transceivers that use different communication protocols. Furthermore, the transceiver may use other protocols such as: wireless local area network (WLAN), wide area network (WAN), or local area network (LAN) protocols such as the Industrial Electrical and Electronics Engineers (IEEE) 802.11, 802.16 and 802.18 standards, Bluetooth and infrared.
The scope of the present embodiment is not limited by the types, the number of, or the frequency of the communication protocols that may be used on a wireless communication device.
It will be understood that the embodiment is also applicable to other types of policy-driven devices or machine-to-machine systems.